{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "import torch as t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        \n",
    "        self.fc1 = nn.Linear(28*28, 300)\n",
    "        self.fc2 = nn.Linear(300, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = x.view(x.shape[0], -1)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.logsigmoid(self.fc2(x))\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mnist_loader import load_data_shared, vectorized_result\n",
    "training_data, validation_data, test_data = load_data_shared(filename=\"../mnist.pkl.gz\",\n",
    "                                                                     seed=666,\n",
    "                                                                     train_size=1000,\n",
    "                                                                     vali_size=0,\n",
    "                                                                     test_size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(data, net):\n",
    "    with t.no_grad():\n",
    "        #for index in range(test_data[0].shape[0]):\n",
    "            # get the inputs\n",
    "        inputs, labels = t.Tensor(data[0]), data[1]\n",
    "\n",
    "        # forward + backward + optimize\n",
    "        outputs = net(inputs)\n",
    "        _, predicted = t.max(outputs, 1)\n",
    "\n",
    "        correct = (predicted == t.Tensor(labels)).sum().item()\n",
    "        accuracy = correct / data[0].shape[0]\n",
    "        return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(net, criterion, optimizer):\n",
    "    loss_scores = []\n",
    "    test_scores = []\n",
    "    train_scores = []\n",
    "    for epoch in range(100):  # loop over the dataset multiple times\n",
    "\n",
    "        # get the inputs\n",
    "        inputs, labels = t.Tensor(training_data[0]), t.Tensor(training_data[1])\n",
    "        vector_labels = t.Tensor([vectorized_result(y) for y in training_data[1]])\n",
    "        # zero the parameter gradients\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # forward + backward + optimize\n",
    "        outputs = net(inputs)\n",
    "        loss = criterion(outputs, labels.long())\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # print statistics\n",
    "        loss_scores.append(loss.item())\n",
    "        train_scores.append(predict(training_data, net))\n",
    "        test_scores.append(predict(test_data, net))\n",
    "    print('Finished Training')\n",
    "    return loss_scores, train_scores, test_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU + Sigmoid + Cross Entropy + L2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "import torch.optim as optim\n",
    "net1 = Net()\n",
    "criterion1 = nn.CrossEntropyLoss()\n",
    "optimizer1 = optim.SGD(net1.parameters(), lr = 1e-1, weight_decay=1e-2)\n",
    "loss_scores1, train_scores1, test_scores1 = fit(net1, criterion1, optimizer1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(loss_scores1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(train_scores1)\n",
    "plt.plot(test_scores1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  ReLU + Sigmoid + Cross Entropy + L2 + RMSprop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "import torch.optim as optim\n",
    "net2 = Net()\n",
    "criterion2 = nn.CrossEntropyLoss()\n",
    "optimizer2 = optim.RMSprop(net2.parameters(), lr = 1e-3, weight_decay=1e-2)\n",
    "loss_scores2, train_scores2, test_scores2 = fit(net2, criterion2, optimizer2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(loss_scores2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(train_scores2)\n",
    "plt.plot(test_scores2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU + Sigmoid + Cross Entropy + L2 + RMSprop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished Training\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch.optim as optim\n",
    "net3 = Net()\n",
    "criterion3 = nn.CrossEntropyLoss()\n",
    "optimizer3 = optim.RMSprop(net3.parameters(), lr = 1e-3, weight_decay=1e-2, alpha=0.5)\n",
    "loss_scores3, train_scores3, test_scores3 = fit(net3, criterion3, optimizer3)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(loss_scores3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(train_scores3)\n",
    "plt.plot(test_scores3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RMSprop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(test_scores1,label=\"score1\")\n",
    "plt.plot(test_scores2,label=\"score2\")\n",
    "plt.plot(test_scores3,label=\"score3\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "net2虽然最后的结果要稍微好一点，但中间的过程有点奇葩，一上一下地跳动很严重。   \n",
    "设置了alpha之后跳动没那么严重了。alpha默认是0.99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
